Parallel Computing for Machine Learning

My name is Xinlei Pan. I am a first year graduate student in bioengineering department. I’m interested in machine learning and its application in biological data analysis. Typically examples include gene expression data analysis to construct gene regulatory network, EEG data analysis to study brain function, etc. For the machine learning part, I have a particular interest in probabilistic graphical model and its application in computational genomics. My general motivation for taking this course is to help with my current research in computational genomics. In recent years, Bayesian networks has been applied in systems biology to uncover genetic regulatory relationships. In this network, the nodes are modeled as genes and the associated variable is gene expression level measured under different experimental conditions. The question is learning the most probable graphical representation of the dependencies among those genes. This can be summarized as a structure learning problem. Exact structure learning of bayesian networks was shown to be NP-complete [1]. I’m interested in seeing how parallel computing can help with this problem.